| 项目编号: | 1704833
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| 项目名称: | GOALI: Collaborative Research: Model-Predictive Safety Systems for Predictive Detection of Operation Hazards |
| 作者: | Warren Seider
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| 承担单位: | University of Pennsylvania
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| 批准年: | 2017
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| 开始日期: | 2017-09-01
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| 结束日期: | 2020-08-31
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| 资助金额: | 107675
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | research project
; research
; researcher
; model-predictive safety system
; model-predictive
; model
; process-model
; process-model parameter value
; model predictive control
; model-based sensor
; process-model mismatch
; research result
; novel safety system
; research team
; process improvement model
; six-month long research internship
; industrial operation
|
| 英文摘要: | Model predictive control is widely being implemented in many industries, such as chemical plants and oil refineries, leading to substantial improvement in operations. The use of process monitoring through model-based sensors has enabled industries to predict and improve processes. Prior research has introduced novel safety systems using models, which generate alarm signals that can provide warnings of pending problems. This research project involves developing a process improvement model will not only prove useful for the chemical and petrochemical industries, but will also benefit the food, nuclear, aircraft, and petroleum industries by identifying potential hazards. Deployment of this model would result in saving lives, reducing workplace injuries, and economic benefits. The researchers are collaborating with the Air Liquide Corporation, which will ensure the industrial relevance and practicality of the results of this research and will enhance the dissemination of research results. The data resulting from this research project will also provide improved security of industrial operations. Additionally, the researchers are developing educational modules and projects based on the outcomes of this research for use in graduate and undergraduate engineering courses at Drexel University and the University of Pennsylvania.
The objectives of this research project are to study: (1) robust large-scale state-estimate prediction (robust to process-model mismatch and unmeasured inputs), (2) offline optimization-based calculation of the worst-case combinations of process-model parameter values and the most extreme control actions, (3) efficient implementation of the model-predictive safety system for large-scale plants, and (4) implementation and testing of the model-predictive safety system first on the steam-drum system of an integrated steam-methane reformer/pressure-swing adsorber unit through simulations, and then on a steam-drum system in a real integrated steam-methane reformer/pressure-swing adsorber system in real time at Air Liquide. The research team also is developing industrial guidelines for adding and maintaining model-predictive safety systems as a complement for existing functional (safety-instrumented) systems. The involvement of the industrial collaborator enriches the training of graduate and undergraduate students involved in the project. The research project also is being integrated with the Drexel Co-op Program, and undergraduate students, preferably from underrepresented groups, are being recruited for six-month long research internships. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/89116
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Warren Seider. GOALI: Collaborative Research: Model-Predictive Safety Systems for Predictive Detection of Operation Hazards. 2017-01-01.
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